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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
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knowledge program analysis, fuzzing, software testing, natural language processing
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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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🎯 Research Vision The next generation of software engineering tools will move beyond autocomplete and static code generation toward autonomous, agentic systems — AI developers capable of planning
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Generative AI NLP skills System security Software testing To be eligible you must have: A first-class honours (H1) Bachelor’s degree or equivalent in the relevant research area (completed or near
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity Monash University MAVERIC is a next-generation, high-performance compute platform and AI supercomputer
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nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions. Required knowledge Data analytics and software
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Nowadays more and more intelligence software solutions emerge in our daily life, for example the face recognition, smart voice assitants, and autonomous vehicle. As a type of data-driven solutions
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Automated software testing and debugging with/without LLMs Primary supervisor Yongqiang Tian Research